Unlocking Unstructured Data
Transforming Public Services with Large Language Models
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Beschreibung
This book explores how Large Language Models can help public organizations turn previously unusable information into actionable insight. Government agencies collect enormous volumes of handwritten forms, PDFs, free-text responses, case notes, and other unstructured content, yet much of it remains difficult to analyze at scale. This book shows how LLMs, combined with OCR, computer vision, and related document-processing techniques, can extract structure and meaning from these data sources, helping public sector teams improve service delivery, operational efficiency, and evidence-based decision-making. It is written for data scientists, AI practitioners, public administrators, policymakers, researchers, and graduate students who want a practical and accessible guide to this fast-emerging field.
Unlocking Unstructured Data: Transforming Public Services with Large Language Models offers a distinctive public-sector perspective on both the technical and organizational challenges of deploying LLMs responsibly. It examines foundational concepts, implementation architectures, and evaluation frameworks, then moves into real-world case studies across healthcare, social services, taxation, regulatory compliance, and citizen engagement. Readers will also find guidance on governance, privacy, explainability, bias mitigation, and change management, making this a useful resource for anyone seeking to modernize government data workflows while maintaining trust, transparency, and accountability.
This book explores how Large Language Models can help public organizations turn previously unusable information into actionable insight. Government agencies collect enormous volumes of handwritten forms, PDFs, free-text responses, case notes, and other unstructured content, yet much of it remains difficult to analyze at scale. This book shows how LLMs, combined with OCR, computer vision, and related document-processing techniques, can extract structure and meaning from these data sources, helping public sector teams improve service delivery, operational efficiency, and evidence-based decision-making. It is written for data scientists, AI practitioners, public administrators, policymakers, researchers, and graduate students who want a practical and accessible guide to this fast-emerging field.
Unlocking Unstructured Data: Transforming Public Services with Large Language Models offers a distinctive public-sector perspective on both the technical and organizational challenges of deploying LLMs responsibly. It examines foundational concepts, implementation architectures, and evaluation frameworks, then moves into real-world case studies across healthcare, social services, taxation, regulatory compliance, and citizen engagement. Readers will also find guidance on governance, privacy, explainability, bias mitigation, and change management, making this a useful resource for anyone seeking to modernize government data workflows while maintaining trust, transparency, and accountability.
Bridges AI theory with real public-sector applications and case studies Integrates technical, operational, and governance perspectives in one framework Focuses on high-stakes domains requiring accuracy, accountability, and trust
Autor*in
Hossein Mohammadi Rouzbahani
Themen in »Unlocking Unstructured Data«
Large Language Models (LLMs) Public Sector AI Document Intelligence Unstructured Data Document Digitization Information Extraction Multimodal AI Regulatory Compliance Social Services Analytics Healthcare Data Processing Citizen Engagement AI Governance Human-in-the-Loop Responsible AI Administrative Decision Support